Wine Quality Predictionand Deployment

Statsly Analytics

By Moumita Das

Problem Statement

The red wine industry shows a recent exponential growth as social drinking is on the rise. Nowadays, industryplayers are using product quality certifications to promote their products. This is a time-consuming processand requires the assessment given by human experts, which makes this process very expensive. Also, the price of red wine depends on a rather abstract concept of wine appreciation by wine tasters,opinions among whom may have a high degree of variability. Another vital factor in red wine certification andquality assessment is physicochemical tests, which are laboratory-based and consider factors like acidity, pHlevel, sugar, and other chemical properties. The red wine market would be of interest if the human quality oftasting can be related to wine’s chemical properties so that certification and quality assessment andassurance processes are more controlled. Determine which features are the best quality red wine indicators and generate insights into each of thesefactors to our model’s red wine quality. Predict the quality of wine on the basis of giving features. Deploy themodel.

Importing all libraries

Import Dataset

Top 5 rows of the Dataset

The description of both numerical & categorical

checking missing values

Data Visualization

Checking for the Outlier and skewnesss

Univariate Analysis

Bivariate analysis

Multivariate Analysis

there are no categorical variables. each feature is a number. Regression problem.

Given the set of values for features, we have to predict the quality of wine.

finding correlation of each feature with our target variable - quality

Data Preprocessing

taking features with correlation more than 0.05 as input x and quality as target variable y

Train and Test Split

determining the shapes of training and testing sets

LinearRegression

predicting the results for the test set

Logistic Regression

Random Forest Classifier

Accuracy Scorez

K-Nearest Neighbor(KNN)

Deployment

Summary